{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title":"人工智能与大数据技术导论","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787302517986","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","id":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","uuid":"cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title":"人工智能与大数据技术导论","normalized_title":"人工智能与大数据技术导论","author":null,"edition":null,"publisher":null,"year":null,"isbn":"9787302517986","isbns":["9787302517986"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":490,"navigation_url":"https://workshop.closeclaw.top/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","title_url":"https://workshop.closeclaw.top/ai/title/%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E4%B8%8E%E5%A4%A7%E6%95%B0%E6%8D%AE%E6%8A%80%E6%9C%AF%E5%AF%BC%E8%AE%BA","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71","page_url":"https://workshop.closeclaw.top/api/ai/books/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/page","search_url":"https://workshop.closeclaw.top/api/ai/books/cd4f7678-cd9b-4971-bde0-e9ad46a6cc71/search"},"page":{"printed_page":457,"pdf_page":473,"physical_index":473,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"e74e676593cb713d03d59eb02a8b4b5ffcba23b19977ddcef869872de9eaa42e"},"chapter":"第16章","content":"I\n\n独立同分布(independently and identically distributed，i.i.d)\n\n从不会改变的分布中提取的数据，其中提取的每个值都不依赖于之前提取的值。i.i.d.是机器学习的理想状态，但在现实世界中几乎从未发现过。例如，某个网页的访问者在短时间内的分布可能为i.i.d.，即分布在短时间内没有变化，且一位用户的访问行为通常与另一位用户的访问行为无关。不过，如果将时间窗口扩大，网页访问者的分布可能呈现出季节性变化。\n\n推断(inference）\n\n在机器学习中，推断的过程通常为：通过将训练过的模型应用于无标签样本来做出预测。在统计学中，推断是指在某些观测数据条件下拟合分布参数的过程。\n\n输入函数(input function)\n\n在TensorFlow中，用于将输入数据返回Estimator的训练、评估或预测方法的函数。例如，训练输入函数用于返回训练集中的批次特征和标签。\n\n输入层(inputlayer)\n\n神经网络中的第一层（接收输入数据的层）。\n\n实例（instance）\n\n样本的同义词。\n\n可解释性（interpretability）\n\n模型的预测可解释的难易程度。深度模型通常不可解释，也就是说，很难对深度模型的不同层进行解释。相比之下，线性回归模型和宽度模型的可解释性通常要好得多。\n\n评分者间一致性信度(inter-rateragreement)\n\n一种衡量指标，用于衡量在执行某项任务时评分者达成一致的频率。如果评分者未达成一致，则可能需要改进任务说明。有时也称为注释者间一致性信度或评分者间可靠性信度。\n\n迭代(iteration)\n\n模型的权重在训练期间的一次更新。迭代包含计算参数在单个批量数据上的梯度损失。\n\nK\n\nKeras\n\n一种热门的Python机器学习API。Keras能够在多种深度学习框架上运行，其中包括TensorFlow（在该框架上，Keras作为tf.keras提供）。\n\n附录D 术语列表\n\n457","previous_page":456,"next_page":458}